An overview of machine learning and big data for drug toxicity evaluation
AH Vo, TR Van Vleet, RR Gupta… - Chemical research in …, 2019 - ACS Publications
Drug toxicity evaluation is an essential process of drug development as it is reportedly
responsible for the attrition of approximately 30% of drug candidates. The rapid increase in …
responsible for the attrition of approximately 30% of drug candidates. The rapid increase in …
Artificial intelligence in drug discovery: a comprehensive review of data-driven and machine learning approaches
As expenditure on drug development increases exponentially, the overall drug discovery
process requires a sustainable revolution. Since artificial intelligence (AI) is leading the …
process requires a sustainable revolution. Since artificial intelligence (AI) is leading the …
The SIDER database of drugs and side effects
Unwanted side effects of drugs are a burden on patients and a severe impediment in the
development of new drugs. At the same time, adverse drug reactions (ADRs) recorded …
development of new drugs. At the same time, adverse drug reactions (ADRs) recorded …
Changing trends in computational drug repositioning
Efforts to maximize the indications potential and revenue from drugs that are already
marketed are largely motivated by what Sir James Black, a Nobel Prize-winning …
marketed are largely motivated by what Sir James Black, a Nobel Prize-winning …
Predicting adverse drug reactions through interpretable deep learning framework
Abstract Background Adverse drug reactions (ADRs) are unintended and harmful reactions
caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the …
caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the …
Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases
The past century has been characterized by intensive efforts, within both academia and the
pharmaceutical industry, to introduce new treatments to individuals with rheumatic …
pharmaceutical industry, to introduce new treatments to individuals with rheumatic …
An explainable supervised machine learning model for predicting respiratory toxicity of chemicals using optimal molecular descriptors
Respiratory toxicity is a serious public health concern caused by the adverse effects of drugs
or chemicals, so the pharmaceutical and chemical industries demand reliable and precise …
or chemicals, so the pharmaceutical and chemical industries demand reliable and precise …
A novel graph attention model for predicting frequencies of drug–side effects from multi-view data
Identifying the frequencies of the drug–side effects is a very important issue in
pharmacological studies and drug risk–benefit. However, designing clinical trials to …
pharmacological studies and drug risk–benefit. However, designing clinical trials to …
Network approaches for modeling the effect of drugs and diseases
The network approach is quickly becoming a fundamental building block of computational
methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of …
methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of …
Five-feature model for developing the classifier for synergistic vs. antagonistic drug combinations built by XGBoost
Combinatorial drug therapy can improve the therapeutic effect and reduce the
corresponding adverse events. In silico strategies to classify synergistic vs. antagonistic drug …
corresponding adverse events. In silico strategies to classify synergistic vs. antagonistic drug …